Improving an ergonomics testing procedure via approximation-based adaptive experimental design

Michael J. Sasena, Matthew Parkinson, Matthew P. Reed, Panos Y. Papalambros, Pierre Goovaerts

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Adaptive design refers to experimental design where the next sample point is determined by information from previous experiments. This article presents a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch, and Jones) that can create adaptive designs using kriging approximations. Our primary goal is to illustrate that superEGO is well-suited to generating adaptive designs which have many advantages over competing methods. The approach is demonstrated on a novel human-reach experiment where the selection of sampling points adapts to the individual test subject. Results indicate that superEGO is effective at satisfying the experimental objectives.

Original languageEnglish (US)
Pages (from-to)1006-1013
Number of pages8
JournalJournal of Mechanical Design - Transactions of the ASME
Issue number5
StatePublished - Sep 2005

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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